Control sampling strategies for case-crossover studies: an assessment of relative efficiency

Am J Epidemiol. 1995 Jul 1;142(1):91-8. doi: 10.1093/oxfordjournals.aje.a117550.


The case-crossover study design is a method to assess the effect of transient exposures on the risk of onset of acute events. Control information for each case is based on his/her past exposure experience, and a self-matched analysis is conducted. Empiric evaluation of five approaches to the analysis of case-crossover data from a study of heavy physical exertion and acute myocardial infarction onset is shown. The data presented are from the Onset Study, a case-crossover study of the determinants of myocardial infarction onset conducted in 45 centers from August 1989 to October 1992. In model 1, exactly one control period (matched on clock-time) was sampled per case. In models 2-4, up to 25 control periods were sampled, and the effect of clock-time on the baseline hazard of infarction was modeled. In model 5, a census of the person-time experienced by each subject over the year preceding the infarction was sampled. The 95% confidence interval for model 1 was 2.7 times wider, and the relative efficiency, defined as v infinity/vM, where vM represents the asymptotic variance estimate of the estimated log relative risk with M control periods sampled per case, was only about 14% of model 5. In models 2-4, the efficiency increased with the number of control periods, regardless of the modeling assumptions. Even with many control periods sampled, models 2-4 achieved only half the efficiency of model 5. The control sampling strategy in any given case-crossover study should be selected with the trade-offs between precision and potential biases of the estimates in mind.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Bias
  • Case-Control Studies*
  • Confounding Factors, Epidemiologic
  • Cross-Over Studies*
  • Exercise
  • Female
  • Humans
  • Male
  • Middle Aged
  • Models, Statistical*
  • Myocardial Infarction / epidemiology
  • Risk